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Statistical insignificance does not suggest the absence of effect, yet scientists must often use null results as evidence of negligible (near-zero) effect size to falsify scientific hypotheses. Doing so must assess a result's null strength, defined as the evidence for a negligible effect size. Such an assessment would differentiate strong null results that suggest a negligible effect size from weak null results that suggest a broad range of potential effect sizes. We propose the most difference in means ($δ_M$) as a two-sample statistic that can both quantify null strength and perform a hypothesis test for negligible effect size. To facilitate consensus when interpreting results, our statistic allows scientists to conclude that a result has negligible effect size using different thresholds with no recalculation required. To assist with selecting a threshold, $δ_M$ can also compare null strength between related results. Both $δ_M$ and the relative form of $δ_M$ outperform other candidate statistics in comparing null strength. We compile broadly related results and use the relative $δ_M$ to compare null strength across different treatments, measurement methods, and experiment models. Reporting the relative $δ_M$ may provide a technical solution to the file drawer problem by encouraging the publication of null and near-zero results.